describe this article and why you feel it is relevant to this class. Your post should be approximately 2-3 paragraphs (150-200 words).
class course :Trevino, L.K. & Nelson, K. A. (2011). Managing business ethics (5th ed.). Hoboken, NJ: John Wiley & Sons, Inc. ISBN: 9780470343944 (print); ISBN: 9780470565964 (e-text).
Ethics is the challenge of distinguishing right from wrong and good from bad. It is challenging because right and good are not always clear. Business intelligence raises many new ethical questions about how we collect intelligence and how we use that intelligence. We are approaching an era in which every BI program will need to actively manage ethics. More data, more kinds of data, and advanced analysis of data often conflict with concerns of data privacy, security, anonymity, and ownership. Besolving these conflicts requires acknowledgment, discussion, and the hard work of defining ethics-based policies and creating a culture of ethical conduct. This article frames the BI ethics challenge by defining ethical questions as distinct from moral and legal questions and by positioning ethical questions in the context of BI. It describes ethical questions and 10 principles of data ethics. Building on this foundation, it describes practices and techniques to guide ethical conduct, to define the business case for ethics management, and to take practical steps to manage BI ethics. [PUBLICATION ABSTRACT]
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James B. Thomann, Ed.D., passed away in April 2011. Jim was a thought leader in information management and business intelligence over a career of more than 30 years, and was recognized as a TDWI Fellow for his substantial and meaningful contributions to the field of BI. Although he was active as a consultant, Jim was a teacher at heart. He found great pleasure and personal fulfillment in helping others to learn. He exhibited strength by overcoming the adversity of a birth defect and accepting no limits to his abilities.
At the time of his death, Jim was deeply engaged in research and investigation into the ethics of BI. It was his strong personal conviction that ethical questions will escalate and that the very important topic of ethics must be addressed. With Jim’s passing, the family asked his long-time friend and colleague, Dave Wells, to complete his unfinished work. This article is the product of that effort and an important part of Jim Thomann’s legacy.
Ethics is the challenge of distinguishing right from wrong and good from bad. It is challenging because right and good are not always clear. Business intelligence raises many new ethical questions about how we collect intelligence and how we use that intelligence. We are approaching an era in which every BI program will need to actively manage ethics. More data, more kinds of data, and advanced analysis of data often conflict with concerns of data privacy, security, anonymity, and ownership. Besolving these conflicts requires acknowledgment, discussion, and the hard work of defining ethics-based policies and creating a culture of ethical conduct.
This article frames the BI ethics challenge by defining ethical questions as distinct from moral and legal questions and by positioning ethical questions in the context of BI. It describes ethical questions and 10 principles of data ethics. Building on this foundation, it describes practices and techniques to guide ethical conduct, to define the business case for ethics management, and to take practical steps to manage BI ethics.
The BI Ethics Dilemma
“There are precious few at ease with moral ambiguities, so we act as though they don’t exist.” This quote from the musical Wicked captures the essence of ethics challenges in business intelligence. Although morals and ethics are somewhat different, they both relate to questions of right and wrong, and both are rich with ambiguity.
Questions of right and wrong are abundant in today’s business climate. The simple view of business purpose as “to create a customer” (Drucker, 1986) has evolved into something much more complex. Balancing multifaceted corporate responsibilities-fiscal, social, and environmental-may raise conflicts and create ethical ambiguities. Doing what is right for shareholders does not always harmonize with doing what is right for consumers. Doing short-term good may produce long-term harm.
BI raises the stakes for business ethics by making corporate conduct visible through data and analytics. It becomes increasingly difficult to ignore ethical questions and to act as though they don’t exist. BI highlights the need for a corporate code of ethics and provides the ability to monitor compliance with that code. Further, BI raises new ethics questions about the methods we use to gather intelligence and the uses to which we apply intelligence.
Herein lies the dilemma for every organization that has implemented BI. The very same technologies and services that we use to inform decision-making processes create the need for new and perhaps more challenging decisions-decisions that resolve ambiguities and answer questions of right and wrong. Ultimately, every BI program needs to intersect with corporate governance to embrace instead of avoid ethical questions.
What is Ethics?
It is difficult to manage BI ethics without first understanding the nature of ethics. We need to define ethics, differentiate what is ethical from what is legal and moral, and position ethics in a business intelligence context.
Richard Hackathorn describes ethics as “a judgment by members of society about what is good or bad behavior” (Hackathorn, 2003). Ethical behavior, then, is doing what we should do. Hackathorn points out that use of the word “should” implies judgment.
Frank Buytendijk says that ethics forms “a code of conduct to refer to in judging what is right and what is wrong” (Buytendijk, 2012) with the premise that ethics is based on core concepts of self, good, and other. Ethical behavior, according to Buytendijk, considers what is good for others as well as what is good for the self.
The concepts of right versus wrong, good versus bad, and judgment are common elements of these definitions that highlight the subjective nature of ethics. Good and bad are subjective; right and wrong are subjective. Subjectivity brings ambiguity and the need for judgment. The foundation of ethical judgments must be solid enough to rationalize and reconcile diverse beliefs- judgment based not on opinion but on values, principles, and critical thinking.
Ethical versus Moral
Although they are closely related, morals and ethics are not synonymous. The role of business is to make ethical judgments, not moral judgments. Both are based on a philosophy of right and wrong, but with distinctly different roots. Ethics relates to a set of rules and guidance that are given to individuals by an external source such as a company or profession. Morals relate to an individual’s personal beliefs and principles about right and wrong.
Every company should provide an ethical framework to guide employee decision making. No company should attempt to prescribe or dictate employee morals. (Note the use of the word “should” in each of the previous two sentences. Both arise from the authors’ judgment.)
Ethical versus Legal
What is legal and what is ethical are related and overlapping concepts, but they are discrete and separate things. What is legal simply refers to actions that comply with laws or statutes. To be ethical means doing the right thing. Clearly, there is more ambiguity in ethical questions than in legal questions. Law is prescriptive. Right and wrong are subjective.
Sometimes what is perceived as unethical is also illegal. However, the converse-the perception that what is legal must also be ethical-is less common. What is legal may still be perceived as unethical, and what is illegal may sometimes be seen as ethical. Perception can shape ethics, but does not shape law. Legal compliance is a mandate for business and corporations. Ethical conduct is a choice.
To give a concrete example: Acme Toyz is an online game service. Users must register in order to play its games, which can be played by anyone, including children under 18. For Acme, the ethical issue is whether it is acceptable (“good”) to collect name and address information from children without their parents’ consent. The moral issue is whether children under the age of 18 should be allowed access to violent games at all (with or without parental consent). The legal issue is whether the company is allowed to collect user data from minors, and if they do collect it, what penalties exist if parents bring a lawsuit against the company.
Ethics in BI
Managing BI ethics involves the creation and oversight of a code of conduct for the way that BI activities are performed. Although legality and morality may be important, they are not concerns of BI ethics management. The goals of BI ethics are to facilitate doing the right things for others and for ourselves and to prevent doing harm to others or to ourselves. Good for others is an important principle of ethical conduct, but it does not imply damage to self in order to achieve good for others.
Three kinds of BI activities are major subjects of ethical consideration in ? I:
1. The ways that we gather data and intelligence
2. The ways that we use data and intelligence
3. The ways that we guide individual and organizational conduct through use of data and intelligence
Figure 1 illustrates the scope of BI ethics considerations.
Each of these areas raises ethical questions. What is right for the customer and what is right for the company when gathering data? What is right for the customer and what is right for the company when using data to drive business results? What is right for employees and what is right for the company when using data to shape organizational conduct?
Probing these questions is certain to expose ambiguities. What is the right thing to do, for example, when customer intelligence gathering without disclosure has substantial business benefit but makes employees uneasy?
In BI, the majority of intelligence gathering is data collection. Ethical issues arise most frequently when collecting data about customers and competitors. When collecting data about customers, two commonly accepted marketing guidelines-do no harm and foster trust-may be good rules of thumb when sorting out uncertainties. Customer and consumer data is perhaps most ethically sensitive because it has the greatest potential to do harm in forms that range from deceptive marketing practices to identity theft.
Where do we get data? Who collects the data? How is the data obtained? The sources from which we collect data, the methods by which we obtain data, and the people who participate raise many questions that can be organized as 10 data ethics principles:
* Informed consent. Should the subject of the data know that data about them is collected, and should their agreement to the data collection activity be required?
* Anonymity. Should all personally identifying information be eliminated from the data? Should data be collected only in the form of aggregates such that individuals can’t be identified?
* Confidentiality. Should sources and providers of data be protected from disclosure?
* Security. To what degree should the data be protected from intrusion, corruption, and unauthorized access?
* Privacy. Should each individual have the ability to control access to personal data about themselves?
* Accuracy. What level of exactness and correctness is required of the data?
* Ownership. Is personal data about individuals an asset that belongs to the business or privately owned information for which the business has stewardship responsibilities?
* Honesty. To what degree should the business be forthright and visible about data collection practices?
* Responsibility. Who is accountable and at what level for use and misuse of data?
* Transparency. On a continuum with polar extremes of “totally open” and “stealth data collection,” what is the right level of transparency?
Fairness is the ultimate ethical question. What is the right balance of good-for-others and good-for-self? The answers to this question may be different for each of the 10 items listed above.
The answers to many ethics questions will vary by subjects and sources of data. Data about business internals, such as processes and employees, is likely to have different ethical considerations than data about markets and competitors that are external to your company. Data about individuals-customers, consumers, and employees-will require different considerations than data about things such as processes, products, and finances. Data may be obtained from internal sources such as operational databases, from tracking website behavior and other customer and consumer interactions, and from publicly available sources such as social media and syndicated data. Each type of data source will vary in its degree of sensitivity to the ethical questions.
The range and scope of ethical questions surrounding data is extensive. Figure 2 illustrates 300 possible domains of data ethics sensitivity-10 data ethics principles, intersecting with three kinds of data sources, and with 10 data subjects spanning market, competitors, and company. The subject list is illustrative, not exhaustive, so the actual scope for any specific application is likely to be much larger.
Ethical questions also apply to data collectors. At each cell of the sensitivity map, identify who collects the data and consider the ethical implications. What roles and responsibilities do employees have in the ethics of data collection? What is their level of awareness and accountability for each of the data ethics principles? What about former employees? What is their responsibility to safeguard the intelligence, information, and data that was accessible during their employment tenure?
Third-party data collectors raise what are perhaps the most complex of data ethics challenges. What are the responsibilities and accountabilities of data sellers, survey services, website visitor tracking services, outsourced call centers, and others who collect data through contractual relationships? How are the responsibilities monitored and enforced?
Further ethical questions arise when you consider the methods used to collect data, especially for customer, consumer, and competitor data. Misrepresentation and manipulation are two very common but ethically questionable methods.
Misrepresentation is the practice of lying about or withholding your identity or role. It is a method of collecting information in a way that is not totally transparent. For example, it is a widely used practice for paid data collectors to collect survey data while claiming to be students working on theses. This is undoubtedly deceptive, but is it unethical? What about attending a competitor’s public events for customers without disclosing your competitive role and interests-is that deceptive or prudent?
Manipulation is the practice of tricking consumers into revealing personal information. It is prevalent in today’s digital economy. Many smartphone apps’ permissions include access to personal information that they don’t really require. Facebook’s App Center is frequently cited as a source of invasive apps. Many e-commerce websites employ a technique known as hidden disclosure-the Web version of “small print.” What about location-sensitive mobile apps? How much data is collected? How long is it retained? How is it used?
Many less deceptive data collection methods are also practiced, including buying data from data sellers, buying data from consumers (offering a gift or discount in exchange for revealing personal data), loyalty programs, and so on. Each method has implications for questions of data ethics principles and sensitivity mapping.
The ways that we use data and intelligence also raise ethical questions. Data itself is neither inherently good nor inherently bad. It is the uses to which we put the data and the intent of those uses that calls for ethical judgment of right versus wrong.
The most common ?I application for information is to support decision-making processes. Good decisions start with good information; two key elements are accuracy and completeness. Thus, choices about what to report and what not to report may be ethical choices. We have the ability to bias decisions: for example, we might amplify some trends while hiding others. Visual data creates many opportunities to add bias, as evidenced by the popular book How to Lie with Statistics. When is it ethical to introduce bias and when is it unethical?
Consumer profiling is an increasingly common application of data. Often unknowingly, consumers provide personal data in everyday activities. Credit card transactions, website browsing, online purchases, ATM usage, social media activities, and mobile phone use all generate data that is collected, aggregated, syndicated, sold, and used to gain insight into consumers and their behaviors. Sometimes the data is used to improve the customer experience; sometimes it is used to drive highly targeted marketing campaigns; sometimes it becomes an additional revenue source when data is resold; sometimes it creates risk of identity theft. Each of these scenarios has implications when considering data ethics principles and the sensitivity map.
A recent trend in consumer profiling is a practice known as persuasion profiling. By tracking consumer responses to carefully configured marketing campaigns, it is possible to determine the types of marketing appeals-social responsibility, popularity, exclusivity, and so on-to which a consumer is most likely to respond. These profiles are used to craft marketing messages tailored to various consumer segments. This is certainly good for the marketer, but is it good for the consumer? When does persuasion cross the line and become manipulation? Where is the balance point in the tension of doing right for the company versus doing right for the consumer?
Ethics also applies to the ways that we distribute data and information. Recall the case of Whole Foods CEO John Mackey. He was caught blogging under a false name, praising his own firm and simultaneously criticizing his competition, Wild Oats. This took place at the time of a potential merger between the two firms. Were his actions unethical? The resulting SEC investigation determined that he had not violated any laws, but we’ve already distinguished between legal and ethical.
Many other uses of information are possible, including reporting, analysis, mining, discovery, fraud detection, and more. Each use has implications for questions of data ethics principles and sensitivity mapping. Intent is a key consideration when evaluating the ethics of using intelligence.
At its core, ethics is about behaviors. The ways we conduct ourselves when self-interest is in conflict with our values, how we behave in the face of ambiguity and uncertainty, the methods we use to make and communicate difficult decisions-these are the tools to create a culture of ethical behaviors. In business and Bl ethics, the purpose is to shape organizational behaviors. Actions, not words, define the character of an organization, and organizational character guides individual conduct.
Bl offers many opportunities to mold organizational culture and character. Using Bl capabilities to detect fraud, both internal and external, is an effective way to expose unethical conduct. Performance management programs can employ metrics and monitoring for compliance with policies that encapsulate ethical positions.
Monitor, manage, and communicate: all of these activities are important when shaping culture. In the end, however, it is what we do that makes a real difference. Making ethical conduct and its consequences highly visible is the best way to create an ethics-focused culture.
Why Ethics Matters in Bl
BI is charged with ambiguities and tension between different perspectives of right and wrong. Decisions about how we gather intelligence, how we use intelligence, and how our actions shape culture and conduct often have far-reaching consequences that are not immediately apparent. The gray areas between certainly right and obviously wrong raise many questions. When using questionable means to gather intelligence to produce an undeniably right outcome, does the end justify the means? If so, what message is sent to the organization? How are culture, conduct, and future ethical decisions influenced?
Recall, for example, the Gordon Gekko line from the 1987 movie Wall Street·. “The point is, ladies and gentlemen, that greed, for lack of a better word, is good. Greed is right, greed works.” This obvious end-justifies-the-means perspective actually suggests that greed is ethical. If it is good and right, then it is also ethical. To challenge this viewpoint, we need to ask the right questions: Greed is good for whom-the self or others? Greed works in what ways-through sustainable gains or quick wins?
What are the side effects and long-term consequences of creating a corporate culture that embraces greed?
Choices of right versus wrong represent only one aspect of the ethical uncertainties and ambiguities in Bl. Equally important, and perhaps even more challenging, are the choices of right versus right. When right-for-self is in conflict with right-for-others, what options are available and what are the consequences of each?
Figure 3 illustrates the dimensions of ambiguity in Bl. Intersecting the questions of right versus wrong with those of right versus right brings many gray areas and little that is crystal clear. Ambiguities are abundant in Bl decision making. We need to become at ease with ambiguities. It is risky to act as if they don’t exist. To acknowledge and openly discuss ethical questions mitigates risk and creates opportunity to realize rewards.
Most of us believe that ethical conduct yields rewards and unethical behavior brings risk. Belief and conviction, however, are often not enough to drive the discussion of ethics. More in-depth rationale is needed to motivate change in the way that we approach ethical questions. Adapting Walter Maner’s levels of justification for computer ethics (Maner, 1996) to the field of business intelligence yields six increasingly strong arguments that build a case for open and transparent discussion of ethical questions:
* Professionalism. We should discuss Bl ethics because doing so will advance the level of professionalism of our Bl program.
* Abuse. We should discuss Bl ethics because doing so will help us to avoid abuse and the negative consequences of abuse.
* Policy gaps. We should discuss Bl ethics because advances in Bl technology and techniques create and will continue to create temporary policy vacuums.
* New policies. We should discuss Bl ethics because the use of Bl permanently transforms some ethical issues to a degree that requires major policy reform.
* New issues. We should discuss Bl ethics because use of Bl technology creates unique ethical issues that require special attention.
* Governance. We should discuss Bl ethics because the set of novel and transformed issues is large enough to define a new domain of corporate governance requirements.
Intersecting these arguments with four common concerns of information management-quality, privacy, security, and compliance-produces 24 discussion areas to build a business case for Bl ethics. Figure 4 illustrates the intersections along with some questions to drive discussion.
Further arguments supporting the business case for ?I ethics are predicated on the concept that Bl ethics is an important subset of corporate ethics, and that corporate ethics produce specific, tangible benefits in several areas, including improvements in:
* Positive reputation and brand value
* Employee commitment and morale
* Ease of employee recruiting and retention
* Access to investment capital
* Customer loyalty
* Financial performance
Many of these benefits are somewhat self evident. Positive reputation is an obvious effect of ethical behaviors. Reputation drives brand value, employee commitment, recruiting of top talent, investor engagement, and customer engagement. Each of these benefits contributes to improved financial performance.
A study published by the Wall Street Journal (Trudel, 2008) offers some quantitative evidence of financial benefits achieved through ethics of corporate social responsibility. In the study’s experiments, groups of consumers were offered the same product (coffee). One group was told that the product was produced with high ethical standards related to labor and ecology. A second group was told that the product was made using unethi- cal methods. A control group was given no information about production ethics. The group with high ethical standards information was willing to pay 17% more for the coffee than the control group. The group with unethi- cal standards information paid nearly 30% less than the control group.
Several similar experiments were conducted with varia- tions of products and pricing models. Although there was some variance in the numbers, each experiment found that customers will pay a premium for perceived high ethical standards and that companies will pay a penalty for perceived unethical conduct.
Managing Bl Ethics
In the final analysis, Bl ethics is a management responsi- bility. Ethical behaviors don’t just happen. Management activities make them happen through policy, communica- tion, and culture. We must become at ease with ethical ambiguities and be willing to acknowledge, discuss, clarify, decide upon, act upon, and continuously learn about them. Bl executives and program managers can manage ethics through ethics-oriented policy, a defined code of ethics, and a continuously evolving culture of ethics.
Ethics policymaking and policy management is proac- tive, responding to ethics questions before they become crises of reputation, public perception, compliance, and litigation. Richard Hackathorn advises a four-step process (Hackathorn, 2003) of recognition, fact finding, deci- sion making, and learning. A variation of Hackathorn’s approach defines tangible actions that managers can take to manage Bl ethics:
* Awareness. Actively seek to identify ethical issues, to describe them objectively using problem-framing techniques, and to invite open discussion and diverse perspectives about each issue.
* Research. Collect as much information as is practical about each issue. Do laws and regulations apply? Are there industry or professional guidelines? What are the beliefs and values of customers, business partners, and the general public? What are the alternatives? What are the potential risks and rewards of each alternative?
* Judgment. Objectively assess the balance of right versus wrong and of right versus right. How much of good- for-self is appropriate? How much of good-for-others is needed? When does the end justify the means? When do the means devalue the end? Have you separated facts from assumptions and applied critical thinking principles? Can you explain the decision rationale to yourself? Can you explain it to others?
* Resolution. Translate judgment to decision. Abstract the judgment to be applied to other similar decisions. Document the decision as policy and define the scope of decisions to which the policy applies.
Although policy encapsulates ethics decisions, it is not necessarily the most effective way to communicate overall corporate values or to provide decision-making guidance. A code of ethics describes conduct guidelines to be used in specific ethical situations. The sensitivity map in Figure 2 (page 22) provides a structure to identify specific ethical situations for BI-the guidelines, for example, for informed consent when collecting data about employees.
To apply code of ethics management practices to a BI program, begin by asking these questions:
* Does your company have a code of ethics?
* Does your industry have a code of ethics?
* Does your profession have a code of ethics?
* Do your governance practices-including corporate governance, data governance, and IT governance- express ethical positions?
In each instance where a code of ethics exists, ask the following questions:
* Does it address BI?
* Does it address or support data and information use?
* Does it provide a structure to recognize and frame ethical questions?
* Does it provide a structure to reason through and resolve ethical questions?
* Does it provide resources to seek ethical guidance?
* Does it include scenarios to understand ethical positions?
Consolidate all of the sources of codified ethical posi- tions, seek and resolve conflicts, and identify and fill gaps. Once you formalize and communicate the BI code of ethics, you’re ready to use it as a tool to shape a culture of ethics. Continuously evolve a culture with these characteristics:
* Openness and honesty
* Transparency of decision processes
* Encouragement of ethical discussions
* Guidance, engagement, and professionalism
* Continuous improvement of processes and practices
* Bounded by mission, values, regulations, and law
BI ethics is not easy, but it is important. It is realistic to expect that it will become an emerging discipline in the next few years. *
Ultimately, every BI program needs to Intersect with corporate governance to embrace Instead of avoid ethical questions.
Legal compliance Is a mandate; ethical conduct is a choice.
Fairness is the ultimate ethical question. What Is the right balance of good-for-others and good-for-self?
Belief and conviction are often not enough to drive the discussion of ethics. More in-depth rationale is needed to motivate change.
Buytendijk, Frank . Socrates Reloaded: The Case for Ethics in Business and Technology, Beingfrank Publications.
Drucker, Peter F. . The Practice of Management, HarperCollins.
Hackathorn, Richard . “Ethics of Business Intelligence, Part 1,” DM Review, June 2003. Retrieved from www.bolder.com/pubs/DMR200306- BI-Ethics.pdf, November 2012.
Maner, Walter . “Unique Ethical Problems in Information Technology,” Science and Engineering Ethics, Volume 2, Number 2, April.
Trudel, Remi, and June Cotte . “Does Being Ethical Pay?” The Wall Street Journal, May 12. Retrieved from http://online.wsj.com/article/ SB121018735490274425.html, November 2012.
James B. Thomann, Ed.D., was a TDWI Fellow and a Bl consultant with DecisionPath Consulting. He was widely recognized as a leading authority on data warehousing, object-oriented methods, and business process analysis. For nearly 30 years, Dr. Thomann provided Bl and information systems consulting and training services to numerous organizations worldwide.
David L. Wells is a consultant and thought leader actively involved in information management, business management, and the intersection of the two. He provides strategic consulting, mentoring, and guidance for business intelligence, performance management, and business analytics programs-the areas where data and information drive business effectiveness, efficiency, and agility.